Publications | |||
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1 |
Yaoliang Yu, Xinhua Zhang, Dale Schuurmans Generalized Conditional Gradient for Sparse Estimation Journal of Machine Learning Research (JMLR) |
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2 |
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan Accelerated Training of Max-Margin Markov Networks with Kernels Journal of Theoretical Computer Science (TCS) Vol 519, pages 88--102, January 2014. [PDF] |
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3 |
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan Smoothing Multivariate Performance Measures Journal of Machine Learning Research (JMLR) |
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4 |
Xiang Yan, Xinhua Zhang, and Liang Huang Computational Analysis and Optimization of the Integrity Distribution Journal of Engineering Mathematics, 20(5), 2003. (in Chinese) [link] |
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Refereed Conference Papers |
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Parameswaran Kamalaruban, Robert C Williamson, Xinhua Zhang Exp-Concavity of Proper Composite Losses Conference on Learning Theory (COLT), 2015. [PDF] |
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Ozlem Aslan, Xinhua Zhang, Dale Schuurmans Convex Deep Learning via Normalized Kernels Advances in Neural Information Processing Systems (NIPS), 2014. [PDF] |
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Changyou Chen, Jun Zhu, Xinhua Zhang Robust Bayesian Max-Margin Clustering Advances in Neural Information Processing Systems (NIPS), 2014. [PDF] [Appendix] |
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Hengshuai Yao, Csaba Szepesvari, Bernardo Avila Pires, Xinhua Zhang Pseudo-MDPs and Factored Linear Action Models Symposium on Adaptive Dynamic Programming and Reinforcement Learning (IEEE ADPRL), 2014. [PDF] |
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Xianghang Liu, Xinhua Zhang, Tiberio Caetano Bayesian Models for Structured Sparse Estimation via Set Cover Prior European Conference on Machine Learning (ECML), 2014. [PDF] [Long] |
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Xinhua Zhang, Wee Sun Lee, Yee Whye Teh Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Advances in Neural Information Processing Systems (NIPS), 2013. [PDF] |
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Xinhua Zhang, Yaoliang Yu, Dale Schuurmans Polar Operators for Structured Sparse Estimation Advances in Neural Information Processing Systems (NIPS), 2013. [PDF] |
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Ozlem Aslan, Hao Cheng, Dale Schuurmans, Xinhua Zhang Convex Two-Layer Modeling Advances in Neural Information Processing Systems (NIPS), 2013. [PDF] |
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Hao Cheng, Xinhua Zhang, Dale Schuurmans Convex Relaxations of Bregman Divergence Clustering Uncertainty in Artificial Intelligence (UAI), 2013. [PDF] |
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Yi Shi, Xinhua Zhang, Xiaoping Liao, Guohui Lin, and Dale Schuurmans Protein-chemical Interaction Prediction via Kernelized Sparse Learning SVM Pacific Symposium on Biocomputing (PSB), 2013. [PDF] |
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Xinhua Zhang, Yaoliang Yu, and Dale Schuurmans Accelerated Training for Matrix-norm Regularization: A Boosting Approach Advances in Neural Information Processing Systems (NIPS), 2012. [PDF] [Code] |
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Martha White, Yaoliang Yu, Xinhua Zhang, and Dale Schuurmans Convex Multi-view Subspace Learning Advances in Neural Information Processing Systems (NIPS), 2012. [PDF] |
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Yi Shi, Xiaoping Liao, Xinhua Zhang, Guohui Lin, and Dale Schuurmans Sparse Learning based Linear Coherent Bi-clustering Workshop on Algorithms in Bioinformatics (WABI), 2012. Lecture Notes in Bioinformatics 7534, 346-364. [PDF] |
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Yaoliang Yu, James Neufeld, Ryan Kiros, Xinhua Zhang, and Dale Schuurmans Regularizers versus Losses for Nonlinear Dimensionality Reduction International Conference on Machine Learning (ICML), 2012. [PDF] [Supplementary] |
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Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan Accelerated Training of Max-Margin Markov Networks with Kernels |
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Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan Smoothing Multivariate Performance Measures Uncertainty in Artificial Intelligence (UAI), 2011. [PDF] [Long] [code] |
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Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, and Dale Schuurmans Convex Sparse Coding, Subspace Learning, and Semi-supervised Extensions AAAI Conference on Artificial Intelligence (AAAI), 2011. [PDF] |
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Ankan Saha, S. V. N. Vishwanathan, Xinhua Zhang New Approximation Algorithms for Minimum Enclosing Convex Shapes ACM-SIAM Syposium on Discrete Algorithms (SODA), 2011. [PDF] |
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Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan Lower Bounds on Rate of Convergence of Cutting Plane Methods Advances in Neural Information Processing Systems (NIPS), 2010. [PDF] [Long] [Detail on Nesterov (arXiv)] [Formalization of weak/strong lower bounds] |
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Xinhua Zhang, Thore Graepel, Ralf Herbrich Bayesian Online Learning for Multi-label and Multi-variate Performance Measures International Conference on Artificial Intelligence and Statistics, (AISTATS) 2010. [PDF] |
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Xinhua Zhang, Le Song, Arthur Gretton, Alex Smola Kernel Measures of Independence for non-iid Data Advances in Neural Information Processing Systems (NIPS), 2008. [PDF] [Appendix] [Spotlight] |
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Le Song, Xinhua Zhang, Alex Smola, Arthur Gretton, and Bernhard Schoelkopf Tailoring Density Estimation via Reproducing Kernel Moment Matching International Conference on Machine Learning (ICML), 2008. [PDF] |
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Li Cheng, S. V. N. Vishwanathan, and Xinhua Zhang Consistent Image Analogies using Semi-supervised Learning IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2008. [PDF] |
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Xinhua Zhang, Douglas Aberdeen, and S. V. N. Vishwanathan Conditional Random Fields for Multi-agent Reinforcement Learning International Conference on Machine Learning (ICML), 2007. [PDF] (Best student paper award) |
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Xinhua Zhang and Wee Sun Lee Hyperparameter Learning for Graph based Semi-supervised Learning Algorithms Advances in Neural Information Processing Systems (NIPS), 2006. [PDF] |
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Xinhua Zhang and Peter K K Loh A Fault-tolerant Routing Strategy for Fibonacci-class Cubes Asia-Pacific Computer Systems Architecture Conference (ACSAC), 2005. [PDF] |
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Refereed Workshop Oral Presentations |
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1 |
Xinhua Zhang, Douglas Aberdeen, and S. V. N. Vishwanathan Conditional Random Fields for Multi-agent Reinforcement Learning Learning Workshop (Snowbird), 2007. [PDF] |
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2 |
Peter K K Loh and Xinhua Zhang A Fault-tolerant Routing Strategy for Gaussian Cube using Gaussian Tree International Conference on Parallel Processing (ICPP) Workshops, 2003. [PDF] |
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Book Chapters |
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1 |
Xinhua Zhang Seven articles: Support vector machines, kernel, regularization, empirical risk minimization, structural risk minimization, covariance matrix, Gaussian distribution. In Claude Sammut and Geoffrey Webb, editors Encyclopedia on Machine Learning. Springer, 2010. |
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Technical Reports |
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1 |
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan Regularized risk minimization by Nesterov’s accelerated gradient methods: Algorithmic extensions and empirical studies http://arxiv.org/abs/1011.0472, 2011. [PDF] |
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Theses | |||
PhD Thesis (Australian National University) | |||
Graphical Models: Modeling, Optimization, and Hilbert Space Embedding [PDF, 3.5 MB] | |||
MSc Thesis (National University of Singapore) | |||
Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms [PDF] | |||
Undergraduate Final Year Project (Nanyang Technological University) | |||
Analysis of Fuzzy-Neuro Network Communications [PDF] |